
def a__       (			A_   ):






  '''simple docstring'''





  if not head:
    return True
  # split the list to two parts
  __magic_name__	,       __magic_name__								=			head.next, head
  while fast and fast.next:
    __magic_name__								=			fast.next.next
    __magic_name__								=			slow.next
  __magic_name__								=			slow.next
  __magic_name__								=			None  # Don't forget here! But forget still works!
  # reverse the second part
  __magic_name__								=			None
  while second:
    __magic_name__								=			second.next
    __magic_name__								=			node
    __magic_name__								=			second
    __magic_name__								=			nxt
  # compare two parts
  # second part has the same or one less node
  while node:
    if node.val != head.val:
      return False
    __magic_name__								=			node.next
    __magic_name__								=			head.next
  return True

def a__       (			A_   ):






  '''simple docstring'''





  if not head or not head.next:
    return True

  # 1. Get the midpoint (slow)
  __magic_name__								=			__magic_name__								=			__magic_name__								=			head
  while fast and fast.next:
    __magic_name__	,       __magic_name__								=			fast.next.next, slow.next

  # 2. Push the second half into the stack
  __magic_name__								=			[slow.val]
  while slow.next:
    __magic_name__								=			slow.next
    stack.append(slow.val   )

  # 3. Comparison
  while stack:
    if stack.pop() != cur.val:
      return False
    __magic_name__								=			cur.next

  return True





def a__       (			A_   ):






  '''simple docstring'''





  if not head or not head.next:
    return True
  __magic_name__								=			{}
  __magic_name__								=			0
  while head:
    if head.val in d:
      d[head.val].append(A_   )
    else:
      __magic_name__								=			[pos]
    __magic_name__								=			head.next
    pos += 1
  __magic_name__								=			pos - 1
  __magic_name__								=			0
  for v in d.values():
    if len(A_   ) % 2 != 0:
      middle += 1
    else:
      __magic_name__								=			0
      for i in range(0,	len(A_   )   ):
        if v[i] + v[len(A_   ) - 1 - step] != checksum:
          return False
        step += 1
    if middle > 1:
      return False
  return True



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						["""image_processor""", """tokenizer"""]
  a__       			=						"""BridgeTowerImageProcessor"""
  a__       			=						("""RobertaTokenizer""", """RobertaTokenizerFast""")
  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    super().__init__(UpperCamelCase__     ,				UpperCamelCase__       )
  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Union[bool, str, PaddingStrategy] = False     ,				UpperCamelCase__     :					Union[bool, str, TruncationStrategy] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				**UpperCamelCase__     :					Dict     ,				)     ->      BatchEncoding:







    """simple docstring"""






    __magic_name__								=			self.tokenizer(
        text=UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				stride=UpperCamelCase__     ,				pad_to_multiple_of=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				return_overflowing_tokens=UpperCamelCase__     ,				return_special_tokens_mask=UpperCamelCase__     ,				return_offsets_mapping=UpperCamelCase__     ,				return_length=UpperCamelCase__     ,				verbose=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    # add pixel_values + pixel_mask
    __magic_name__								=			self.image_processor(
        UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				do_normalize=UpperCamelCase__     ,				do_center_crop=UpperCamelCase__     ,				**UpperCamelCase__       )
    encoding.update(UpperCamelCase__       )

    return encoding
  def 				_lowercase				(					self     :					Optional[Any]     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__     ,				**UpperCamelCase__       )





  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer.model_input_names
    __magic_name__								=			self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

